Foundations of Genetic Algorithms: 9th International Workshop, FOGA 2007, Mexico City, Mexico, January 8-11, 2007, Revised Selected PapersChristopher R. Stephens Readers will find here a fascinating text that is the thoroughly refereed post-proceedings of the 9th Workshop on the Foundations of Genetic Algorithms, FOGA 2007, held in Mexico City in January 2007. The 11 revised full papers presented were carefully reviewed and selected during two rounds of reviewing and improvement from 22 submissions. The papers address all current topics in the field of theoretical evolutionary computation and also depict the continuing growth in interactions with other fields such as mathematics, physics, and biology |
Contents
Inbreeding Properties of Geometric Crossover and Nongeometric | 1 |
Just What Are Building Blocks? | 15 |
Sufficient Conditions for CoarseGraining Evolutionary Dynamics | 35 |
On the Brittleness of Evolutionary Algorithms | 54 |
Mutative Selfadaptation on the Sharp and Parabolic Ridge | 70 |
Genericity of the Fixed Point Set for the Infinite Population Genetic | 97 |
Neighborhood Graphs and Symmetric Genetic Operators | 110 |
Decomposition of Fitness Functions in Random Heuristic Search | 123 |
On the Effects of BitWise Neutrality on Fitness Distance Correlation | 138 |
Continuous Optimisation Theory Made Easy? FiniteElement | 165 |
Saddles and Barrier in Landscapes of Generalized Search Operators | 194 |
Author Index | 213 |
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Common terms and phrases
analysis applied approach approximation assume bits block bound called coarse-grained Computation connected consider continuous corresponding defined definition denote depends derived different difficulty distance distribution dynamics effect elements encoding equations evolution evolutionary evolutionary algorithms Evolutionary Computation example exists expected finite first fitness function fixed Genetic Algorithms genomes genotypic geometric crossover given gives global graph increase landscape linear Markov chain mean measure metric multiset mutation rates mutation strength natural neighborhood neutrality Note obtain offspring operators optimization optimum parameter parents Parity partitioning performance phenotypic population positive possible predicted probability problem Proof properties prove random Real recombination represent representation respect ridge runs sample schema search space selection simple solution string structure Theorem theory tion Truth Table values variables vector